





'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase									=   logging.getLogger()

lowerCAmelCase									=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 		lowerCamelCase	(   _A							):



	def 	_lowerCamelCase     ( self		,   a_						):
			os.makedirs(a_		,   exist_ok=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= {"source": "What is love ?", "target": "life"}
			lowerCAmelCase  :     Any       						= {"train": 12, "val": 2, "test": 2}
			for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
							lowerCAmelCase  :     List[str]       						= "\n".join([contents[field]] * n_lines[split]						)
							with open(os.path.join(a_		,   F'''{split}.{field}'''						)		,   "w"						) as f:
									f.write(a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = "pytorch"						):
			lowerCAmelCase  :     int       						= self.get_auto_remove_tmp_dir()
			lowerCAmelCase  :     Any       						= os.path.join(a_		,   "output"						)
			lowerCAmelCase  :     str       						= os.path.join(a_		,   "data"						)
			self._create_dummy_data(data_dir=a_						)

			lowerCAmelCase  :     str       						= F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

			if gpus > 0:
					testargs.append(F'''--gpus={gpus}'''						)
					if is_apex_available():
							testargs.append("--fp16"						)
			else:
					testargs.append("--gpus=0"						)
					testargs.append("--distributed_backend=ddp_cpu"						)
					testargs.append("--num_processes=2"						)

			lowerCAmelCase  :     Dict       						= [sys.executable, str(Path(finetune_rag.__file__						).resolve()						)] + testargs
			execute_subprocess_async(a_		,   env=self.get_env()						)

			lowerCAmelCase  :     Optional[int]       						= os.path.join(a_		,   "metrics.json"						)
			with open(a_						) as f:
					lowerCAmelCase  :     Union[str, Any]       						= json.load(a_						)
			return result



	@require_torch_gpu
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= self._run_finetune(gpus=1						)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,   0.2						)



	@require_torch_multi_gpu
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self._run_finetune(gpus=2						)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,   0.2						)



	@require_torch_gpu
	@require_ray
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self._run_finetune(gpus=1		,   distributed_retriever="ray"						)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,   0.2						)





	@require_torch_multi_gpu
	@require_ray
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self._run_finetune(gpus=1		,   distributed_retriever="ray"						)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,   0.2						)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



